Determinants of chronic disease patients' intention to use Internet diagnosis and treatment services: based on the UTAUT2 model.

Background: Chronic diseases are a significant public health concern. Internet diagnosis and treatment services can effectively monitor chronic diseases and are vital for alleviating the healthcare system burden caused by these conditions. Distinguishing itself from prior investigations, this study focuses on the critical cohort of chronic disease patients and, building upon the UTAUT2 framework, introduces additional constructs such as trust and medical habits. It systematically examines the pivotal determinants influencing the acceptance and utilization of Internet diagnosis and treatment services among chronic disease patients in Shenzhen, China.

Objective: This study centers on the population of chronic disease patients in Shenzhen, China, by developing a theoretical model to elucidate their behavioral intentions toward utilizing Internet diagnosis and treatment services. Employing empirical methods, the research identifies the key determinants that influence patients' acceptance and adoption of these services. Furthermore, based on the interactive mechanisms among these factors, targeted policy recommendations are advanced to enhance service utilization rates and optimize the quality of Internet diagnosis and treatment services.

Methods: Guided by the theoretical framework, and informed by expert consultations and a preliminary survey, the questionnaire was meticulously designed and refined. Employing a five-point Likert scale, the survey investigated the usage patterns of Internet diagnosis and treatment services among chronic disease patients in Shenzhen, China, as well as the factors influencing their behavioral intention. Utilizing convenience sampling, a total of 823 valid responses were collected. Subsequent data analysis was conducted using SPSS 26.0 and AMOS 28.0 software, encompassing descriptive statistics and structural equation modeling. Furthermore, the Bootstrap method was employed to rigorously assess the mediating effects within the model.

Results: The empirical findings reveal that: (1) Model validation indicates that performance expectancy (β = 0.151, p = 0.002), effort expectancy (β = 0.105, p = 0.022), social influence (β = 0.206, p < 0.001), price value (β = 0.138, p = 0.002), trust (β = 0.124, p = 0.003), and electronic health literacy (β = 0.184, p < 0.001) exert significant positive effects on the behavioral intention to use Internet diagnosis and treatment services. Conversely, perceived risk negatively influences behavioral intention (β = 0.094, p = 0.008), whereas the effect of medical habits on behavioral intention is not statistically significant (p > 0.05). (2) Performance expectancy partially mediates the relationships between effort expectancy, trust, electronic health literacy, and behavioral intention, while effort expectancy partially mediates the relationship between electronic health literacy and behavioral intention.

Conclusion: Performance expectancy, effort expectancy, social influence, price value, trust, perceived risk, and electronic health literacy constitute the principal determinants shaping the behavioral intention of chronic disease patients to adopt Internet diagnosis and treatment services. Drawing on these findings, this study advances targeted policy recommendations aimed at optimizing user experience and fostering the sustainable, high-quality development of Internet diagnosis and treatment services within chronic disease management.